Artificial intelligence course
Artificial intelligence (AI) is the science and engineering of creating intelligent machines and systems that can perform tasks that normally require human intelligence and abilities. AI has many applications and benefits for various domains, such as business, education, health, entertainment, and social good. AI also poses some challenges and risks, such as ethical, social, and environmental issues, that need to be addressed and regulated.
This introductory course in artificial intelligence aims to provide the learners with a basic understanding of the concepts, principles, and techniques of AI, as well as expose them to some of the current and emerging trends and topics in AI research and practice.
Objectives
The learning objectives the course are:
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To explain what is AI, its history, its goals, and its main branches and subfields.
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To understand the basic methods and algorithms of AI, such as search, logic, probability, machine learning, deep learning, neural networks, natural language processing, computer vision, robotics, and game theory.
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To apply the AI methods and algorithms to solve simple problems or tasks using appropriate tools and frameworks.
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To evaluate the performance and limitations of AI methods and algorithms in different scenarios and contexts.
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To explore the real-world applications and use cases of AI in various domains and industries.
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To identify the ethical, social, legal, and environmental implications and challenges of AI for individuals, organizations, and society.
An introductory course in artificial intelligence is suitable for anyone who is interested in learning about AI or wants to start a career in AI. No prior advanced programming or computer science background is required, but some basic programming mathematical and logical skills are helpful.
Schedule
- Introduction (1 week)
- Solving Problems by Searching (2 week)
- Complex Searching Approaches (2 week)
- Constraint Satisfaction Problems (1 week)
- Adversarial Search and Games (1 week)
- Logical Agents (1 week)
- Machine Learning (3 week)
- Natural language processing (2 week)
- Advanced Topics (Deep learning) (2 week)
- Exams (1 week)
Grading policy
- Homework – individual works (4-5 points)
- Writing assignments (WAs)
- Programming assignments (PAs)
- Project – works in groups of three students (4 points)
- Class activities (1 point)
- Midterm exam – paper based/ practical exam (4-5 points)
- Final exam – paper based (6 points)
- Additional activities -- works in groups of three students ([−1, 3] points)
- Research work with in-class presentations
- We have time for three in class presentation
- Homework design
- Course material design
- Research work with in-class presentations
Instructor
Morteza Zakeri, Ph.D.
ACM Professional Member